Model-Based Threat Assessment for Avoiding Arbitrary Vehicle Collisions

Abstract

This paper presents a model-based algorithm that estimates how the driver of a vehicle can either steer, brake, or accelerate to avoid colliding with an arbitrary object. In this algorithm, the motion of the vehicle is described by a linear bicycle model, and the perimeter of the vehicle is represented by a rectangle. The estimated perimeter of the object is described by a polygon that is allowed to change size, shape, position, and orientation at sampled time instances. Potential evasive maneuvers are modeled, parameterized, and approximated such that an analytical expression can be derived to estimate the set of maneuvers that the driver can use to avoid a collision. This set of maneuvers is then assessed to determine if the driver needs immediate assistance to avoid or mitigate an accident. The proposed threat-assessment algorithm is evaluated using authentic data from both real traffic conditions and collision situations on a test track and by using simulations with a detailed vehicle model. The evaluations show that the algorithm outperforms conventional threat-assessment algorithms at rear-end collisions in terms of the timing of autonomous brake activation. This is crucial for increasing the performance of collisionavoidance systems and for decreasing the risk of unnecessary braking. Moreover, the algorithm is computationally efficient and can be used to assist the driver in avoiding or mitigating collisions with all types of road users in all kinds of traffic scenarios.

A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies

“…Conclusive answers to these questions have been a long-standing objective of traffic safety research, and have a range of implications: In the design of roads, vehicles, or vehicle support systems for safety and automation, quantitative models of driver behavior can be very directly applied, for example in system algorithms or in computer simulations of crashes (e.g., Perel, 1982;Fambro et al 2000a;MacAdam, 2001;Brännström et al, 2010;Markkula, 2015). In the broader study of traffic safety, the way one thinks about drivers' emergency responses can also be important in more subtle ways, for example by shaping design of experiments and subsequent interpretations of results, or by guiding one's analysis of actual crashes to understand their causation (e.g., Naing et al, 2009;Engström et al, 2013b), sometimes for purposes of litigation (e.g., Maddox and Kiefer, 2012).…”

Set-Based Prediction of Traffic Participants on Arbitrary Road Networks

“…In [16], only a single behavior of each surrounding traffic participant is predicted to compute possible evasive trajectories of the ego vehicle. Single trajectories of other road users are considered in [17] in order to determine whether a collision should be avoided by steering and/or braking. Also, the threat assessment of traffic situations is performed in [18] by considering multiple possible maneuvers of the ego vehicle, but only a single future trajectory of surrounding vehicles.…”

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

“…The TTR thus additionally takes possible, future evasive driver actions into account. The same holds for the two approaches described in [44,129]. In the former, a critical situation, which results in triggering an autonomous 7 Note that combining Fuzzy and probability theory as done in some mentioned approaches lacks a theoretical basis as a fuzziness cannot be interpreted or converted to a probability.…”